[R] extracting bootstrap statistics by group with loop

[Rui Barradas]

Hello,

Now I'm spamming the list, not one of my days.

My first post was right, there was no bug and the 2nd one was exactly 
the same code, it corrected nothing at all.

Apologies for the noise,

Rui Barradas

Às 16:55 de 31/10/21, Rui Barradas escreveu:
> Hello,
> 
> Sorry, bug. In both by instructions it's boot_mean_se, not bootprop.
> 
> 
> boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R = R)
> boot_year_area <- by(DaT,
>                       INDICES = list(Year = DaT$Year, Area = DaT$Area),
>                       FUN = boot_mean_se,
>                       statistic = bootprop, R = R)
> 
> 
> Hope this helps,
> 
> Rui Barradas
> 
> Às 16:48 de 31/10/21, Rui Barradas escreveu:
>> Hello,
>>
>> Try to aggregate with ?by.
>>
>>
>> bootprop <- function(data, index){
>>    d <- data[index, ]
>>    sum(d[["bothTimes"]], na.rm = TRUE)/sum(d[["total"]], na.rm = TRUE)#
>> }
>> boot_mean_se <- function(data, statistic, R){
>>    b <- boot::boot(DaT, bootprop, R = R)
>>    c(bootMean = mean(b$t), bootSE = sd(b$t))
>> }
>>
>> boot_year <- by(DaT, DaT$Year, boot_mean_se, statistic = bootprop, R = R)
>> boot_year_area <- by(DaT,
>>                       INDICES = list(Year = DaT$Year, Area = DaT$Area),
>>                       FUN = boot_mean_se,
>>                       statistic = bootprop, R = R)
>> boot_year
>> boot_year_area
>>
>> boot_year <- do.call(rbind, boot_year)
>>
>> d <- dimnames(boot_year_area)
>> boot_year_area <- cbind(Reduce(expand.grid, rev(d))[2:1],
>>                          do.call(rbind, boot_year_area))
>> names(boot_year_area)[1:2] <- names(d)
>> boot_year_area
>>
>>
>> Hope this helps,
>>
>> Rui Barradas
>>
>> Às 11:47 de 31/10/21, Marna Wagley escreveu:
>>> Hi R users,
>>> I was trying to extract the bootstrap mean and its SE by group but I 
>>> have
>>> been doing  it by separating the group manually. The data set is big so
>>> doing it manually is a kind of tedious task. I am wondering whether 
>>> there
>>> is a possibility to do it by creating a loop. I am weak in writing loop
>>> functions. I am attaching an example data and how I performed the
>>> analysis, see below.
>>> Thanks for your help.
>>> Sincerely,
>>> MW
>>> ####
>>> library(boot)
>>> DaT<-structure(list(bothTimes = c(0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L,
>>> 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L),
>>> total = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
>>> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), Area = c("A",
>>> "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B", "A", "A",
>>> "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"), Year = c(2015L, 
>>> 2015L,
>>> 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L, 2015L,
>>> 2015L, 2015L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L, 2016L,
>>> 2016L, 2016L, 2016L, 2016L, 2016L)), class = "data.frame", row.names =
>>> c(NA, -24L))
>>>
>>> head(DaT)
>>> R=100
>>> bootprop <- function(data, index){
>>>    d <- data[index, ]
>>>    sum(d[["bothTimes"]], na.rm = TRUE)/sum(d[["total"]], na.rm = TRUE)#
>>> }
>>>
>>> ###################
>>> #2015
>>> ###################
>>> #-----Year2015_pooled
>>> Y2015_pooled<-subset(DaT, DaT$Year=="2015")
>>> Y2015_pooled_boot <- boot(Y2015_pooled, bootprop, R)
>>> boot_Y2015_pooled<-data.frame(Year="2015", Area= "Pooled", bootMean=
>>> Y2015_pooled_boot$t0, SE=sd(Y2015_pooled_boot$t))
>>> #-----Year2015_AreaA
>>> Y2015_A<-subset(DaT, DaT$Year=="2015" & DaT$Area=="A")
>>> Y2015_A_boot <- boot(Y2015_A, bootprop, R)
>>> boot_Y2015_A<-data.frame(Year="2015", Area= "A", bootMean= 
>>> Y2015_A_boot$t0,
>>> SE=sd(Y2015_A_boot$t))
>>> #----Year2015_AreaB
>>> Y2015_B<-subset(DaT, DaT$Year=="2015" & DaT$Area=="B")
>>> Y2015_B_boot <- boot(Y2015_B, bootprop, R)
>>> boot_Y2015_B<-data.frame(Year="2015", Area= "B", bootMean= 
>>> Y2015_B_boot$t0,
>>> SE=sd(Y2015_B_boot$t))
>>> ###################
>>> #2016
>>> ###################
>>> #-----Year2016_pooled
>>> Y2016_pooled<-subset(DaT, DaT$Year=="2016")
>>> Y2016_pooled_boot <- boot(Y2016_pooled, bootprop, R)
>>> boot_Y2016_pooled<-data.frame(Year="2016", Area= "Pooled", bootMean=
>>> Y2016_pooled_boot$t0, SE=sd(Y2016_pooled_boot$t))
>>>
>>> #-----Year2016_AreaA
>>> Y2016_A<-subset(DaT, DaT$Year=="2016" & DaT$Area=="A")
>>> Y2016_A_boot <- boot(Y2016_A, bootprop, R)
>>>
>>> boot_Y2016_A<-data.frame(Year="2016", Area= "A", bootMean= 
>>> Y2016_A_boot$t0,
>>> SE=sd(Y2016_A_boot$t))
>>> #----Year2016_AreaB
>>> Y2016_B<-subset(DaT, DaT$Year=="2016" & DaT$Area=="B")
>>> Y2016_B_boot <- boot(Y2016_B, bootprop, R)
>>> boot_Y2016_B<-data.frame(Year="2016", Area= "B", bootMean= 
>>> Y2016_B_boot$t0,
>>> SE=sd(Y2016_B_boot$t))
>>>
>>> ## output data.matrix
>>> BootMean_All<-rbind(boot_Y2015_pooled,boot_Y2015_A,boot_Y2015_B,boot_Y2016_pooled,boot_Y2016_A,boot_Y2016_B) 
>>>
>>> BootMean_All
>>>
>>>     [[alternative HTML version deleted]]
>>>
>>> ______________________________________________
>>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
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>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>> ______________________________________________
>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide 
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
> 
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide 
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.